Designing an intelligent control system for drones using evolutionary algorithms

The rapid advancement of unmanned aerial vehicle (UAV) technology over the past decade has highlighted the need for intelligent control systems with high adaptability, stability, and efficiency. Designing controllers capable of reliable performance under dynamic uncertainties, environmental variations, and computational constraints remains a significant challenge. In recent years, evolutionary algorithms (EAs) have emerged as powerful optimization techniques for developing intelligent UAV control systems. This paper provides a comprehensive review of the application of various evolutionary algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Evolutionary Strategies (ES), in the design and tuning of UAV controllers. The review discusses the comparative performance of these methods in areas such as PID and fuzzy controller tuning, flight path optimization, and multi-UAV coordination control. Challenges such as computational cost, local convergence, and real-time implementation are also addressed. The findings suggest that hybrid approaches combining evolutionary algorithms with machine learning and adaptive control techniques represent a promising direction for the future development of intelligent UAV control systems

کلیدواژه ها:

UAV ، intelligent control system ، evolutionary algorithms ، genetic algorithm ، particle swarm optimization ، fuzzy control ، controller design